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Wang, Kun

Publications and source records attributed to Wang, Kun.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Overexpression of a gibberellin 20-oxidase gene in poplar xylem led to an increase in the size of nanocellulose fibrils and improved paper properties

Cellulose, the major component of secondary cell walls, is the most abundant renewable long-chain polymer on earth. Nanocellulose has become a prominent nano-reinforcement agent for polymer matrices in various industries. We report the generation of transgenic hybrid poplar overexpressing the Arabidopsis gibberellin 20-oxidase1 gene driven by a xylem-specific promoter to increase gibberellin (GA) biosynthesis in wood. X-ray diffraction (XRD) and sum frequency generation spectroscopic (SFG) analyses showed that cellulose in transgenic trees was less crystalline, but the crystal size was larger. The nanocellulose fibrils prepared from transgenic wood had an increased size compared to those from wild type. When such fibrils were used as a reinforcing agent in sheet paper preparation, the mechanical strength of the paper was significantly enhanced. Engineering the GA pathway can therefore affect nanocellulose properties, providing a new strategy for expanding nanocellulose applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Accelerated Synthesis of Ordered Mesoporous Carbons Using Plasma

Conventional ordered mesoporous carbon (OMC) production usually requires long processing times in the carbonization step to achieve desired temperatures through controlled ramps. To enable expedited materials discovery, developing advanced manufacturing capability with significantly improved throughput is highly desired. Current approaches for accelerating the synthesis of OMCs include using microwave and Joule heating. However, both methods rely on the introduction of additional components, such as microwave absorbers and electrically conductive agents, within the bulk materials to impart the ability to reach high carbonization temperatures. This work demonstrates accelerated synthesis and functionalization of OMCs through the use of a dielectric barrier discharge plasma, where carbonization can be accomplished within 15 min using 30 W plasma sources, representing more than an order of magnitude increase in polymer-to-carbon conversion kinetics compared to that of a traditionally pyrolyzed analogue. Particularly, the ability of performing rapid carbonization without the use of additional substrates within the OMC precursor systems is advantageous. A systematic investigation of how plasma power, time, and gas atmosphere impact the resulting OMC pore textures and properties is performed, demonstrating the broad applicability of plasma-enabled carbonization methods. Furthermore, we demonstrate that the plasma treatment strategy can be extended to incorporate heteroatoms into the carbon framework by introducing ammonia gas, resulting in OMCs with a nitrogen content up to 4.7 at %, as well as non-Pluronic templating systems for synthesizing OMC with pore sizes larger than 10 nm. As employing a plasma source for materials pyrolysis is an industrially relevant approach, our system can be extended toward scaled synthesis of OMCs with much faster production rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Hourglass–Shaped Nanocages with Concaved Structures Based on Selective Self–Complementary Coordination Ligands and Tunable Hierarchical Self–Assembly

Three-dimensional (3D) structures constructed via coordination-driven self-assemblies have recently garnered increasing attention due to the challenges in structural design and potential applications. In particular, developing new strategy for the convenient and precise self-assemblies of 3D supramolecular structures is of utmost interest. Introducing the concept of self-coordination ligands, herein the design and synthesis of two meta-modified terpyridyl ligands with selective self-complementary coordination moiety are reported and their capability to assemble into two hourglass-shaped nanocages SA and SB is demonstrated. Within these 3D structures, the meta-modified terpyridyl unit preferably coordinates with itself to serve as concave part. By changing the arm length of the ligands, hexamer (SA) and tetramer (SB) are obtained respectively. In-depth studies on the assembly mechanism of SA and SB indicate that the dimers could be formed first via self-complementary coordination and play crucial roles in controlling the final structures. Moreover, both SA and SB can go through hierarchical self-assemblies in solution as well as on solid–liquid interface, which are characterized by transmission electron microscope (TEM) and scanning tunneling microscopy (STM). As a result, it is further demonstrated that various higher-order assembly structures can be achieved by tuning the environmental conditions.

3D metallo-supramolecules↗
DOE OSTI2022

Room temperature slot-die coated perovskite layer modified with sulfonyl-γ-AApeptide for high performance perovskite solar devices

Perovskite solar cells (PSCs) exhibited remarkable progress for small aperture area cells, however, the performance of its counterpart, large aperture area cells, lags due to non-uniform and defective perovskite layers. Here, we fabricate reproducible large area homogeneous perovskite films at room temperature and without controlling humidity (up to 40 % RH) with a slot die coater on a c-TiO 2 layer deposited using a large area chemical bath. A new artificial peptide – sulfonyl-γ-AApeptide (F-GLU-S) was employed to modify the slot-die coated perovskite surface, grain boundaries and electronic defects. The multi-functional F-GLU-S with carbonyl, carboxyl, sulfonyl, benzene, and chloro groups was capable of strongly interacting with the perovskite layer and repairing the uncoordinated Pb 2+ ions and halide vacancies. As a result, both the electron and hole densities of defects were significantly suppressed; consequently, the non-radiative recombination was effectively suppressed for the modified device which can be explicitly seen in the device performance where both V oc and FF of the modified device improved considerably. Therefore, F-GLU-S modified slot-die coated MAPbI 3 – based devices demonstrated outstanding performance of 21.44 % PCE with a V oc of 1.13 V, J sc of 24.64 cm –2 , and FF 76.99 %. Furthermore, F-GLU-S passivation impeded the infiltration of moisture and oxygen due to its hydrophobic nature and defect repair potential. As a result, the modified device retained above 92 % of its original PCE after 720 h in air (room temperature and 40–60 % RH).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Single-Molecule Chemical Reactions Unveiled in Molecular Junctions

Understanding chemical processes at the single-molecule scale represents the ultimate limit of analytical chemistry. Single-molecule detection techniques allow one to reveal the detailed dynamics and kinetics of a chemical reaction with unprecedented accuracy. It has also enabled the discoveries of new reaction pathways or intermediates/transition states that are inaccessible in conventional ensemble experiments, which is critical to elucidating their intrinsic mechanisms. Thanks to the rapid development of single-molecule junction (SMJ) techniques, detecting chemical reactions via monitoring the electrical current through single molecules has received an increasing amount of attention and has witnessed tremendous advances in recent years. Research efforts in this direction have opened a new route for probing chemical and physical processes with single-molecule precision. This review presents detailed advancements in probing single-molecule chemical reactions using SMJ techniques. We specifically highlight recent progress in investigating electric-field-driven reactions, reaction dynamics and kinetics, host–guest interactions, and redox reactions of different molecular systems. Finally, we discuss the potential of single-molecule detection using SMJs across various future applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Domain wall enabled steep slope switching in MoS2 transistors towards hysteresis-free operation

Abstract The device concept of ferroelectric-based negative capacitance (NC) transistors offers a promising route for achieving energy-efficient logic applications that can outperform the conventional semiconductor technology, while viable operation mechanisms remain a central topic of debate. In this work, we report steep slope switching in MoS 2 transistors back-gated by single-layer polycrystalline PbZr 0.35 Ti 0.65 O 3 . The devices exhibit current switching ratios up to 8 × 10 6 within an ultra-low gate voltage window of $$V_{{{\mathrm{g}}}} = \pm \! 0.5$$ V g = ± 0.5 V and subthreshold swing (SS) as low as 9.7 mV decade −1 at room temperature, transcending the 60 mV decade −1 Boltzmann limit without involving additional dielectric layers. Theoretical modeling reveals the dominant role of the metastable polar states within domain walls in enabling the NC mode, which is corroborated by the relation between SS and domain wall density. Our findings shed light on a hysteresis-free mechanism for NC operation, providing a simple yet effective material strategy for developing low-power 2D nanoelectronics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predicting Future Laboratory Fault Friction Through Deep Learning Transformer Models

Machine learning models using seismic emissions as input can predict instantaneous fault characteristics such as displacement and friction in laboratory experiments, and slow slip in Earth. Here, we address whether the seismic/acoustic emission (AE) from laboratory experiments contains information about future frictional behavior. The approach uses a convolutional encoder-decoder containing a transformer model in the latent space, similar to models used for natural language processing. We test the model limits using progressively larger AE input time windows and progressively larger output friction time windows. The results demonstrate that very near-term friction predictions are indeed contained in the AE signal, and predictions are progressively worse farther into the future. The future predictions by the model of impending failure in the near-term are remarkably robust. This first effort predicting future fault frictional behavior with machine learning will aid in guiding efforts for applications in Earth.

58 GEOSCIENCES↗
DOE OSTI2022

labquake_future_prediction

The labquake_future_prediction code is a collection of python modules and scripts that serves as supporting information for the article “Predicting future laboratory fault friction through deep learning” for publication in the journal of “Geophysical Research Letters”. It is designed to predict laboratory fault slips in the immediate future by scanning continuous acoustic emission (AE) waveforms recorded in laboratory biaxial shear experiments. The predictions are made with a deep learning model based on convolutional encoder-decoder (CED) models and the Transformer model primarily developed for Natural Language Processing (NLP). The deep learning model is trained with the tensorflow package using publicly available laboratory data sets in standard binary file format in numpy. The utility functions for reading data files, configuring model hyperparameters, constructing the CED and Transformer models, training and testing of the models are defined in python module files. The workflow of training the models for labquake future predictions and the multiple GPU’s rapid model hyperparameter optimization as described in the journal article, are demonstrated in accompanying python script files and Jupyter notebooks.

Wang, Kun↗
DOE OSTI2022

Switching the conductance of a single molecule: Lessons from molecular junctions

The concept of using single molecules as functional building blocks in electronic circuitry represents the ultimate device miniaturization and has garnered tremendous attention from physics, chemistry, and engineering. Recent advances in single-molecule junction techniques have enabled direct investigation of charge transport in a quasi-one-dimensional conduction channel composed of an individual molecule. Furthermore, one of the main research focuses over the past decade has been to understand the conductance switching effect of single molecules as it is essential for developing molecular-scale optoelectronics, computing, and sensing applications. In this perspective, we highlight the established physical and chemical mechanisms for single-molecule conductance switching behavior and examine the associated stimuli in single-molecule junction systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Beyond electrical conductance: progress and prospects in single-molecule junctions

The idea of using individual molecules as conducting wires, regulators, and interconnects for charges in electronic circuitry has catalyzed the vibrant development of the field of single-molecule electronics. The ability to reliably and repeatedly construct single-molecule junctions (SMJs) has enabled the study of charge transport through a broad spectrum of individual molecules. Over the past decade, the capability of the SMJ platform has been greatly expanded thanks to the development of new experimental techniques and the integration of knowledge and methodologies from other disciplines. New opportunities beyond electrical conductance have emerged at the atomic and molecular scale. It has been demonstrated in many recent studies that the SMJ can now serve as a powerful and versatile tool to address critical physical and chemical questions that are otherwise inaccessible. Furthermore, this perspective focuses on the new capabilities and functionalities of the SMJ beyond conductance measurements and how they have enabled the investigation of crucial single-molecule processes and advanced our understanding beyond molecular electronics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Effect of Electroacupuncture at Wushu Acupoints of the Cardiopulmonary Meridian on the Autophagy in Rats with Acute Myocardial Ischemia

Wushu acupoints are the five acupoints distributed below the human elbow and knee joint. They are all located on the same meridian and divided into five categories: Jing, Ying, Shu, Jing, and He. It has been shown that electroacupuncture (EA) at Shenmen point of heart meridian can improve acute myocardial ischemia (AMI) early. However, it is still unclear if all the Wushu acupoints of the heart meridian can improve AMI. Hence, this study emphasizes Wushu acupoints of heart meridian, compares them with Wushu acupoints of lung meridian, and studies the therapeutic effect of EA at Wushu acupoints on AMI and its possible mechanism. It also discusses the specificity of the heart meridian to heart disease. The AMI model is established by ligation of the left anterior descending coronary artery. The detection methods like the physiological recorder, TTC staining, ELISA, and so forth were used to determine the ECG, myocardial infarct size, serum myocardial enzymes, and myocardial tissue-related protein expression in rats. The heart rate (HR) and ST segment along with creatine kinase (CK), creatine kinase isoenzymes (CK-MB), lactate dehydrogenase (LDH), and myocardial infarctions increased after the induction with AMI. Furthermore, the expressions of PINK1 and Parkin protein also showed an increase. However, EA at Wushu acupoints in the heart meridian can reverse the above changes, whereas EA at the lung meridian exhibits limited effect. It is depicted that the heart meridian has a relatively specific relationship with the heart in a diseased state.

Zhu, Chao↗
DOE OSTI2022

Light-Driven Charge Transport and Optical Sensing in Molecular Junctions

Probing charge and energy transport in molecular junctions (MJs) has not only enabled a fundamental understanding of quantum transport at the atomic and molecular scale, but it also holds significant promise for the development of molecular-scale electronic devices. Recent years have witnessed a rapidly growing interest in understanding light-matter interactions in illuminated MJs. These studies have profoundly deepened our knowledge of the structure–property relations of various molecular materials and paved critical pathways towards utilizing single molecules in future optoelectronics applications. In this article, we survey recent progress in investigating light-driven charge transport in MJs, including junctions composed of a single molecule and self-assembled monolayers (SAMs) of molecules, and new opportunities in optical sensing at the single-molecule level. We focus our attention on describing the experimental design, key phenomena, and the underlying mechanisms. Specifically, topics presented include light-assisted charge transport, photoswitch, and photoemission in MJs. Emerging Raman sensing in MJs is also discussed. Finally, outstanding challenges are explored, and future perspectives in the field are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Predicting fault slip via transfer learning

Abstract Data-driven machine-learning for predicting instantaneous and future fault-slip in laboratory experiments has recently progressed markedly, primarily due to large training data sets. In Earth however, earthquake interevent times range from 10’s-100’s of years and geophysical data typically exist for only a portion of an earthquake cycle. Sparse data presents a serious challenge to training machine learning models for predicting fault slip in Earth. Here we describe a transfer learning approach using numerical simulations to train a convolutional encoder-decoder that predicts fault-slip behavior in laboratory experiments. The model learns a mapping between acoustic emission and fault friction histories from numerical simulations, and generalizes to produce accurate predictions of laboratory fault friction. Notably, the predictions improve by further training the model latent space using only a portion of data from a single laboratory earthquake-cycle. The transfer learning results elucidate the potential of using models trained on numerical simulations and fine-tuned with small geophysical data sets for potential applications to faults in Earth.

58 GEOSCIENCES↗
DOE OSTI2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗